Hashgraph-Based Model Parameter Management for Reliable and Secure Deep Learning

Deep learning networks have rapidly developed across various fields, with several high-performing models already being applied in real-world applications. However, even with identical network architectures, variations in parameter values can lead to significantly different outputs, making the security of these parameters crucial for maintaining network performance. Despite their importance, limited research has focused on securing deep learning network parameters. In recent years, distributed ledger technologies, such as blockchain and hashgraph, have been extensively studied. Their applications extend beyond cryptocurrencies to include public services, voting, supply chains, and insurance. This paper proposes a novel approach to enhancing the security of deep learning network parameters by leveraging Hedera Hashgraph, providing a more secure foundation for their deployment and commercialization. With the security offered by hashgraph, model parameters are safely stored, ensuring the reliability and trustworthiness of deep learning systems. Additionally, when these stored parameters are used for further training, such as in transfer learning, the results are expected to be reliable. We demonstrate that our proposed system secures the parameters effectively, guaranteeing both security and reliability in deep learning.